Random forest model

In this exercise, you will use the randomForest() function in the randomForest package to build a random forest model for predicting churn of the customers in the training data set, training_set. The target variable is called Future. You will also inspect and visualize the importance of the variables in the model.

Este ejercicio forma parte del curso

Predictive Analytics using Networked Data in R

Ver curso

Instrucciones del ejercicio

Load the randomForest package.
Use the set.seed() function with the seed 863.
Build a random forest using the function randomForest() and all the variables in training_set. The response variable Future needs to be a factor, so utilize the as.factor() function.
Plot the variable importance of the random forest model using varImpPlot().

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Load package
___(randomForest)

# Set seed
set.seed(___)

# Build model
rfModel <- ___(as.factor(___)~. ,data=training_set)

# Plot variable importance
varImpPlot(___)

Editar y ejecutar código